library(groundhog)
pkgs <- c("tidyverse","here", "lmerTest", "sjPlot","broom.mixed", "kableExtra", "ggeffects", "gt", "brms", "bayestestR","ggdist", "pheatmap", "heatmaply","pheatmap","gplots","RColorBrewer", "tm", "wordcloud", "psych")
groundhog.day <- '2022-07-25'
groundhog.library(pkgs, groundhog.day)
The package 'tidyverse_1.3.2' is already attached.
The package 'here_1.0.1' is already attached.
The package 'lmerTest_3.1-3' is already attached.
The package 'sjPlot_2.8.10' is already attached.
The package 'broom.mixed_0.2.9.4' is already attached.
The package 'kableExtra_1.3.4' is already attached.
The package 'ggeffects_1.1.2' is already attached.
The package 'gt_0.6.0' is already attached.
The package 'brms_2.17.0' is already attached.
The package 'bayestestR_0.12.1' is already attached.
The package 'ggdist_3.2.0' is already attached.
The package 'pheatmap_1.0.12' is already attached.
The package 'heatmaply_1.3.0' is already attached.
The package 'pheatmap_1.0.12' is already attached.
The package 'gplots_3.1.3' is already attached.
The package 'RColorBrewer_1.1-3' is already attached.
The package 'tm_0.7-8' is already attached.
The package 'wordcloud_2.6' is already attached.
The package 'psych_2.2.5' is already attached.
here::i_am("Analysis/idmPrelimAnal.Rmd")
here() starts at /Users/jacobelder/Documents/GitHub/EpMemNet
devtools::source_url("https://raw.githubusercontent.com/JacobElder/MiscellaneousR/master/corToOne.R")
devtools::source_url("https://raw.githubusercontent.com/JacobElder/MiscellaneousR/master/plotCommAxes.R")
devtools::source_url("https://raw.githubusercontent.com/JacobElder/MiscellaneousR/master/named.effects.ref.R")
fullLong <- arrow::read_parquet(here("Data", "longEpMNet.parquet"))
fullShort <- arrow::read_parquet(here("Data","shortEpMNet.parquet"))
fullLong$subID <- as.numeric(fullLong$subID)
fullData <- fullLong %>% full_join(fullShort, by = c("subID"))
# How many people listed 0 connections?
nrow(fullShort[which(fullShort$edgeTot==0),])
#describe(fullLong$strength)
#Create a vector containing only the text
text <- as.vector(fullData$memory)
# Create a corpus
docs <- Corpus(VectorSource(text))
docs <- docs %>%
tm_map(removeNumbers) %>%
tm_map(removePunctuation) %>%
tm_map(stripWhitespace)
docs <- tm_map(docs, content_transformer(tolower))
docs <- tm_map(docs, removeWords, stopwords("english"))
docs <- tm_map(docs, removeWords, c("the","and"))
dtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
words <- sort(rowSums(matrix),decreasing=TRUE)
df <- data.frame(word = names(words),freq=words)
set.seed(24)
wordcloud(words = df$word, freq = df$freq, min.freq = 1, max.words=200, random.order=FALSE, rot.per=0.35, colors=brewer.pal(8, "Dark2"))
Yes, the farther away in time, the more experiences something causes.
m<-glmer(outdegree ~ scale(length) + numID + ( scale(length) | subID), data=fullData,family="poisson")
summary(m)
Yes, the farther back in time, the fewer experiences cause something.
m<-glmer(indegree ~ scale(length) + numID + ( scale(length) | subID), data=fullData,family="poisson")
summary(m)
More positive and negative, experience causes more experience
More positive, experience is caused by more experiences
summary(m)
Generalized linear mixed model fit by maximum likelihood (Laplace
Approximation) [glmerMod]
Family: poisson ( log )
Formula:
indegree ~ scale(positive) * scale(negative) + numID + (scale(positive) +
scale(negative) | subID)
Data: fullData
AIC BIC logLik deviance df.resid
5027.8 5085.5 -2502.9 5005.8 1393
Scaled residuals:
Min 1Q Median 3Q Max
-2.3843 -0.6552 -0.1208 0.2958 8.3101
Random effects:
Groups Name Variance Std.Dev. Corr
subID (Intercept) 0.235059 0.48483
scale(positive) 0.003621 0.06017 0.04
scale(negative) 0.060790 0.24656 0.19 0.98
Number of obs: 1404, groups: subID, 204
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.094788 0.082784 -1.145 0.25220
scale(positive) 0.126108 0.038491 3.276 0.00105 **
scale(negative) 0.069826 0.049892 1.400 0.16165
numID 0.035238 0.004767 7.391 1.45e-13 ***
scale(positive):scale(negative) 0.046105 0.025669 1.796 0.07248 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) scl(p) scl(n) numID
scale(pstv) 0.105
scale(ngtv) 0.195 0.640
numID -0.780 0.023 0.020
scl(pst):() 0.174 -0.182 0.143 0.005
optimizer (Nelder_Mead) convergence code: 0 (OK)
Model failed to converge with max|grad| = 0.0979906 (tol = 0.002, component 1)
More positive and more negative, experience causes more experiences
m<-glmer(outdegree ~ scale(PANAS_P) + scale(PANAS_N) + numID + ( scale(PANAS_P) + scale(PANAS_N) | subID), data=fullData,family="poisson")
summary(m)
m<-glmer(outdegree ~ scale(PANAS_1) + numID + ( scale(PANAS_1) | subID), data=fullData,family="poisson")
summary(m)
m<-glmer(outdegree ~ scale(PANAS_2) + numID + ( scale(PANAS_2) | subID), data=fullData,family="poisson")
summary(m)
m<-glmer(outdegree ~ scale(PANAS_3) + numID + ( scale(PANAS_3) | subID), data=fullData,family="poisson")
summary(m)
m<-glmer(outdegree ~ scale(PANAS_4) + numID + ( scale(PANAS_4) | subID), data=fullData,family="poisson")
summary(m)
m<-glmer(outdegree ~ scale(PANAS_5) + numID + ( scale(PANAS_5) | subID), data=fullData,family="poisson")
summary(m)
m<-glmer(outdegree ~ scale(PANAS_6) + numID + ( scale(PANAS_6) | subID), data=fullData,family="poisson")
summary(m)
m<-glmer(outdegree ~ scale(PANAS_7) + numID + ( scale(PANAS_7) | subID), data=fullData,family="poisson")
summary(m)
m<-glmer(outdegree ~ scale(PANAS_8) + numID + ( scale(PANAS_8) | subID), data=fullData,family="poisson")
summary(m)
m<-glmer(outdegree ~ scale(PANAS_9) + numID + ( scale(PANAS_9) | subID), data=fullData,family="poisson")
summary(m)
m<-glmer(outdegree ~ scale(PANAS_10) + numID + ( scale(PANAS_10) | subID), data=fullData,family="poisson")
summary(m)
More positive, more experiences cause an experience
m<-glmer(indegree ~ scale(PANAS_P) + scale(PANAS_N) + numID + ( scale(PANAS_P) + scale(PANAS_N) | subID), data=fullData,family="poisson")
summary(m)
m<-glmer(indegree ~ scale(PANAS_1) + numID + ( scale(PANAS_1) | subID), data=fullData,family="poisson")
summary(m)
m<-glmer(indegree ~ scale(PANAS_2) + numID + ( scale(PANAS_2) | subID), data=fullData,family="poisson")
summary(m)
m<-glmer(indegree ~ scale(PANAS_3) + numID + ( scale(PANAS_3) | subID), data=fullData,family="poisson")
summary(m)
m<-glmer(indegree ~ scale(PANAS_4) + numID + ( scale(PANAS_4) | subID), data=fullData,family="poisson")
summary(m)
m<-glmer(indegree ~ scale(PANAS_5) + numID + ( scale(PANAS_5) | subID), data=fullData,family="poisson")
summary(m)
m<-glmer(indegree ~ scale(PANAS_6) + numID + ( scale(PANAS_6) | subID), data=fullData,family="poisson")
summary(m)
m<-glmer(indegree ~ scale(PANAS_7) + numID + ( scale(PANAS_7) | subID), data=fullData,family="poisson")
summary(m)
m<-glmer(indegree ~ scale(PANAS_8) + numID + ( scale(PANAS_8) | subID), data=fullData,family="poisson")
summary(m)
m<-glmer(indegree ~ scale(PANAS_9) + numID + ( scale(PANAS_9) | subID), data=fullData,family="poisson")
summary(m)
m<-glmer(indegree ~ scale(PANAS_10) + numID + ( scale(PANAS_10) | subID), data=fullData,family="poisson")
summary(m)
Causing more experiences and being caused by more experiences is associated with greater certainty in experience, but causing is a stronger effect.
Using strength/similarity is stronger effect.
m<-lmer(scale(Cert) ~ scale(outdegree) + scale(indegree) + numID + scale(length) + ( scale(outdegree) + scale(indegree) | subID), data=fullData)
summary(m)
m<-lmer(scale(Cert) ~ scale(strengthIn) + scale(strengthOut) + ( scale(strengthIn) + scale(strengthOut) | subID), data=fullData)
summary(m)
Experiences causing more experiences are more predictive of clarity than experiences caused by more experiences
m<-lmer( scale(Clear) ~ scale(outdegree) + scale(indegree) + numID + scale(length) + ( scale(outdegree) + scale(indegree) | subID), data=fullData)
summary(m)
m<-lmer( scale(Clear) ~ scale(strengthIn) + scale(strengthOut) + numID + scale(length) + ( scale(strengthIn) + scale(strengthOut) | subID), data=fullData)
summary(m)
The number of an experiences of causes, but not what it is caused by, predict how fundamental an experience is.
m<-lmer( scale(Fund) ~ scale(outdegree) + scale(indegree) + numID + scale(length) + ( scale(outdegree) + scale(indegree) | subID), data=fullData)
summary(m)
m<-lmer( scale(Fund) ~ scale(strengthIn) + scale(strengthOut) + numID + scale(length) + ( scale(strengthIn) + scale(strengthOut) | subID), data=fullData)
summary(m)
m<-lmer(scale(PCAimp) ~ scale(outdegree) + scale(indegree) + numID + scale(length) + ( scale(outdegree) + scale(indegree) | subID), data=fullData)
summary(m)
m<-lmer(scale(PCAimp) ~ scale(strengthOut) + scale(strengthIn) + numID + scale(length) + ( scale(strengthOut) + scale(strengthIn) | subID), data=fullData)
summary(m)
Experiences that cause more experiences are perceived as important to self, but not experiences that are caused by more experiences.
m<-lmer( scale(IM) ~ scale(outdegree) + scale(indegree) + numID + scale(length) + ( scale(outdegree) + scale(indegree) | subID), data=fullData)
summary(m)
m<-lmer( scale(IM) ~ scale(strengthOut) + scale(strengthIn) + numID + scale(length) + ( scale(strengthOut) + scale(strengthIn) | subID), data=fullData)
summary(m)
Experiences that cause more experiences are perceived as important to others, but not experiences that are caused by more experiences.
m<-lmer( scale(IO) ~ scale(outdegree) + scale(indegree) + numID + scale(length) + ( scale(outdegree) + scale(indegree) | subID), data=fullData)
summary(m)
m<-lmer( scale(IO) ~ scale(strengthOut) + scale(strengthIn) + numID + scale(length) + ( scale(strengthOut) + scale(strengthIn) | subID), data=fullData)
summary(m)
No evidence
fullData$ImpDiff <- (fullData$IM-fullData$IO)
m<-lmer( scale(ImpDiff) ~ scale(strengthOut) + scale(strengthIn) + numID + scale(length) + ( scale(strengthOut) + scale(strengthIn) | subID), data=fullData)
summary(m)
m<-lmer( scale(ImpDiff) ~ scale(outdegree) + scale(indegree) + numID + scale(length) + ( scale(outdegree) + scale(indegree) | subID), data=fullData)
summary(m)
Experiences with more causes and caused by more are reflected on more frequently. Perhaps some stronger effects for causing more.
m<-lmer( scale(Often) ~ scale(outdegree) + scale(indegree) + numID + scale(length) + ( scale(outdegree) + scale(indegree) | subID), data=fullData)
summary(m)
m<-lmer( scale(Often) ~ scale(strengthOut) + scale(strengthIn) + numID + scale(length) + ( scale(strengthOut) + scale(strengthIn) | subID), data=fullData)
summary(m)
m<-lmer( scale(Chan) ~ scale(outdegree) + scale(indegree) + numID + scale(length) + ( scale(outdegree) + scale(indegree) | subID), data=fullData)
summary(m)
m<-lmer( scale(Chan) ~ scale(strengthOut) + scale(strengthIn) + numID + scale(length) + ( scale(strengthOut) + scale(strengthIn) | subID), data=fullData)
summary(m)
People feel experiences with more causes are more representative. Similar, but weaker, effect for experiences caused by more experiences.
m<-lmer( scale(Rep) ~ scale(outdegree) + scale(indegree) + numID + scale(length) + ( scale(outdegree) + scale(indegree) | subID), data=fullData)
summary(m)
m<-lmer( scale(Rep) ~ scale(strengthOut) + scale(strengthIn) + numID + scale(length) + ( scale(strengthOut) + scale(strengthIn) | subID), data=fullData)
summary(m)
Experiences caused by more experiences have more qualitative positive sentiment.
m<-lmer( scale(vad_comp) ~ scale(outdegree) + scale(indegree) + numID + scale(length) + ( scale(outdegree) + scale(indegree) | subID), data=fullData)
summary(m)
m<-lmer( scale(vad_comp) ~ scale(strengthOut) + scale(strengthIn) + numID + scale(length) + ( scale(strengthOut) + scale(strengthIn) | subID), data=fullData)
summary(m)
Experiences with more experiences causing them are qualitatively more positive
m<-lmer( scale(vad_pos) ~ scale(outdegree) + scale(indegree) + numID + scale(length) + ( scale(outdegree) + scale(indegree) | subID), data=fullData)
summary(m)
m<-lmer( scale(vad_pos) ~ scale(strengthOut) + scale(strengthIn) + numID + scale(length) + ( scale(strengthOut) + scale(strengthIn) | subID), data=fullData)
summary(m)
No negative effects
m<-lmer( scale(vad_neg) ~ scale(outdegree) + scale(indegree) + numID + scale(length) + ( scale(outdegree) + scale(indegree) | subID), data=fullData)
summary(m)
m<-lmer( scale(vad_neg) ~ scale(strengthOut) + scale(strengthIn) + numID + scale(length) + ( scale(strengthOut) + scale(strengthIn) | subID), data=fullData)
summary(m)
No effect of causes or caused by on number of words
summary(m)
Generalized linear mixed model fit by maximum likelihood (Laplace
Approximation) [glmerMod]
Family: poisson ( log )
Formula: nwords ~ outdegree + indegree + numID + scale(length) + (outdegree +
indegree | subID)
Data: fullData
AIC BIC logLik deviance df.resid
12504.6 12567.3 -6241.3 12482.6 2204
Scaled residuals:
Min 1Q Median 3Q Max
-6.3076 -0.7071 -0.1331 0.5397 12.0926
Random effects:
Groups Name Variance Std.Dev. Corr
subID (Intercept) 0.568425 0.75394
outdegree 0.002340 0.04838 0.12
indegree 0.002723 0.05218 -0.38 0.26
Number of obs: 2215, groups: subID, 215
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.9334800 0.0811461 23.827 <2e-16 ***
outdegree -0.0123978 0.0077872 -1.592 0.111
indegree -0.0005592 0.0090943 -0.061 0.951
numID 0.0006189 0.0056419 0.110 0.913
scale(length) 0.0862643 0.0097761 8.824 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) outdgr indegr numID
outdegree -0.099
indegree -0.081 0.116
numID -0.738 0.044 -0.178
scal(lngth) -0.034 -0.047 0.085 0.014
Experiences with more causes are more broad.
m<-lmer( scale(Breadth) ~ scale(outdegree) + scale(indegree) + numID + scale(length) + ( scale(outdegree) + scale(indegree) | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: scale(Breadth) ~ scale(outdegree) + scale(indegree) + numID +
scale(length) + (scale(outdegree) + scale(indegree) | subID)
Data: fullData
REML criterion at convergence: 5255.5
Scaled residuals:
Min 1Q Median 3Q Max
-2.38126 -0.64650 -0.00537 0.65203 2.99689
Random effects:
Groups Name Variance Std.Dev. Corr
subID (Intercept) 0.254937 0.50491
scale(outdegree) 0.033977 0.18433 -0.08
scale(indegree) 0.001751 0.04185 -0.01 0.94
Residual 0.703320 0.83864
Number of obs: 1975, groups: subID, 209
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 2.435e-01 7.291e-02 2.532e+02 3.339 0.000967 ***
scale(outdegree) 9.815e-02 3.180e-02 5.899e+01 3.086 0.003086 **
scale(indegree) -2.257e-02 2.359e-02 4.733e+00 -0.957 0.385073
numID -1.430e-02 4.654e-03 1.716e+02 -3.072 0.002472 **
scale(length) 4.908e-02 2.276e-02 1.916e+03 2.157 0.031163 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) scl(t) scl(n) numID
scale(tdgr) 0.224
scale(ndgr) 0.144 0.113
numID -0.800 -0.187 -0.114
scal(lngth) -0.086 -0.075 0.134 0.051
m<-lmer( scale(Breadth) ~ scale(strengthOut) + scale(strengthIn) + numID + scale(length) + ( scale(strengthOut) + scale(strengthIn) | subID), data=fullData)
boundary (singular) fit: see help('isSingular')
Warning: Model failed to converge with 1 negative eigenvalue: -1.6e+03
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: scale(Breadth) ~ scale(strengthOut) + scale(strengthIn) + numID +
scale(length) + (scale(strengthOut) + scale(strengthIn) | subID)
Data: fullData
REML criterion at convergence: 5276
Scaled residuals:
Min 1Q Median 3Q Max
-2.3666 -0.6810 -0.0020 0.6681 3.1794
Random effects:
Groups Name Variance Std.Dev. Corr
subID (Intercept) 0.2663596 0.51610
scale(strengthOut) 0.0004799 0.02191 1.00
scale(strengthIn) 0.0055609 0.07457 0.24 0.24
Residual 0.7270389 0.85267
Number of obs: 1975, groups: subID, 209
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 2.239e-01 7.090e-02 2.030e+02 3.158 0.00183 **
scale(strengthOut) 6.527e-02 2.299e-02 4.132e+02 2.840 0.00474 **
scale(strengthIn) 7.710e-03 2.789e-02 2.932e+00 0.276 0.80054
numID -1.265e-02 4.571e-03 1.580e+02 -2.767 0.00633 **
scale(length) 6.804e-02 2.257e-02 1.890e+03 3.015 0.00261 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) scl(O) scl(I) numID
scl(strngO) 0.125
scl(strngI) 0.135 -0.107
numID -0.782 -0.044 -0.060
scal(lngth) -0.064 -0.036 0.152 0.030
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')
Experiences with more causes are perceived as more distinct/different.
m<-lmer( scale(Dist) ~ scale(outdegree) + scale(indegree) + numID + scale(length) + ( scale(outdegree) + scale(indegree) | subID), data=fullData)
boundary (singular) fit: see help('isSingular')
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula:
scale(Dist) ~ scale(outdegree) + scale(indegree) + numID + scale(length) +
(scale(outdegree) + scale(indegree) | subID)
Data: fullData
REML criterion at convergence: 5135.8
Scaled residuals:
Min 1Q Median 3Q Max
-3.9072 -0.5359 0.1170 0.6383 2.6875
Random effects:
Groups Name Variance Std.Dev. Corr
subID (Intercept) 0.2933625 0.54163
scale(outdegree) 0.0256675 0.16021 -0.38
scale(indegree) 0.0002987 0.01728 -0.55 -0.56
Residual 0.6454951 0.80343
Number of obs: 1985, groups: subID, 209
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 1.726e-01 7.529e-02 2.591e+02 2.292 0.02270 *
scale(outdegree) 8.732e-02 2.912e-02 8.263e+01 2.999 0.00358 **
scale(indegree) 3.735e-02 2.118e-02 6.440e+02 1.763 0.07837 .
numID -9.835e-03 4.891e-03 1.879e+02 -2.011 0.04577 *
scale(length) -8.188e-04 2.192e-02 1.945e+03 -0.037 0.97020
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) scl(t) scl(n) numID
scale(tdgr) 0.189
scale(ndgr) 0.117 -0.125
numID -0.806 -0.264 -0.151
scal(lngth) -0.087 -0.081 0.142 0.056
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')
m<-lmer( scale(Dist) ~ scale(strengthOut) + scale(strengthIn) + numID + scale(length) + ( scale(strengthOut) + scale(strengthIn) | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: scale(Dist) ~ scale(strengthOut) + scale(strengthIn) + numID +
scale(length) + (scale(strengthOut) + scale(strengthIn) | subID)
Data: fullData
REML criterion at convergence: 5121.1
Scaled residuals:
Min 1Q Median 3Q Max
-3.8859 -0.5442 0.1147 0.6225 2.6721
Random effects:
Groups Name Variance Std.Dev. Corr
subID (Intercept) 0.291166 0.53960
scale(strengthOut) 0.019970 0.14132 -0.53
scale(strengthIn) 0.003752 0.06126 0.02 0.22
Residual 0.641282 0.80080
Number of obs: 1985, groups: subID, 209
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 1.969e-01 7.513e-02 2.660e+02 2.621 0.00927 **
scale(strengthOut) 1.358e-01 2.827e-02 5.135e+01 4.804 1.39e-05 ***
scale(strengthIn) 1.568e-02 2.575e-02 1.906e+01 0.609 0.54975
numID -1.147e-02 4.844e-03 1.927e+02 -2.367 0.01894 *
scale(length) -6.898e-04 2.178e-02 1.930e+03 -0.032 0.97474
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) scl(O) scl(I) numID
scl(strngO) 0.207
scl(strngI) 0.120 -0.078
numID -0.805 -0.319 -0.085
scal(lngth) -0.079 -0.063 0.139 0.050
fullData$SminO <- fullData$SO_1 - fullData$SO_2
m<-lmer( SminO ~ SE + ( 1 | subID), data=fullData)
summary(m)
m<-lmer(Chan ~ page + ( page | subID), data=fullData)
Warning: Model failed to converge with max|grad| = 0.00201605 (tol = 0.002, component 1)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Chan ~ page + (page | subID)
Data: fullData
REML criterion at convergence: 7451.1
Scaled residuals:
Min 1Q Median 3Q Max
-3.5506 -0.5188 0.1435 0.6380 2.4692
Random effects:
Groups Name Variance Std.Dev. Corr
subID (Intercept) 0.7239 0.8508
page 2.8202 1.6794 -0.37
Residual 1.8739 1.3689
Number of obs: 2067, groups: subID, 211
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 5.28397 0.09343 139.75154 56.555 <2e-16 ***
page 0.53139 0.39896 155.86021 1.332 0.185
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
page -0.686
optimizer (nloptwrap) convergence code: 0 (OK)
Model failed to converge with max|grad| = 0.00201605 (tol = 0.002, component 1)
m<-lmer(Chan ~ pageW + ( pageW | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Chan ~ pageW + (pageW | subID)
Data: fullData
REML criterion at convergence: 7450
Scaled residuals:
Min 1Q Median 3Q Max
-3.5729 -0.5228 0.1492 0.6423 2.4633
Random effects:
Groups Name Variance Std.Dev. Corr
subID (Intercept) 0.7045 0.8393
pageW 2.1633 1.4708 -0.31
Residual 1.8739 1.3689
Number of obs: 2067, groups: subID, 211
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 5.25817 0.09159 146.51342 57.41 <2e-16 ***
pageW 0.68403 0.38436 163.86429 1.78 0.077 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
pageW -0.669
m<-lmer(Chan ~ pageOut + ( pageOut | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Chan ~ pageOut + (pageOut | subID)
Data: fullData
REML criterion at convergence: 7423
Scaled residuals:
Min 1Q Median 3Q Max
-3.5889 -0.5275 0.1288 0.6339 2.2266
Random effects:
Groups Name Variance Std.Dev. Corr
subID (Intercept) 0.9007 0.949
pageOut 5.2869 2.299 -0.70
Residual 1.8388 1.356
Number of obs: 2067, groups: subID, 211
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 5.18192 0.09275 168.21829 55.872 < 2e-16 ***
pageOut 1.32770 0.34736 165.28688 3.822 0.000187 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
pageOut -0.705
m<-lmer(Chan ~ pageOutW + ( pageOutW | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Chan ~ pageOutW + (pageOutW | subID)
Data: fullData
REML criterion at convergence: 7424.5
Scaled residuals:
Min 1Q Median 3Q Max
-3.5684 -0.5271 0.1284 0.6323 2.2400
Random effects:
Groups Name Variance Std.Dev. Corr
subID (Intercept) 0.8643 0.9297
pageOutW 4.8983 2.2132 -0.65
Residual 1.8400 1.3565
Number of obs: 2067, groups: subID, 211
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 5.16388 0.09199 168.14558 56.134 < 2e-16 ***
pageOutW 1.40907 0.35061 164.58161 4.019 8.88e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
pageOutW -0.692
m<-lmer(Chan ~ hub + ( hub | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Chan ~ hub + (hub | subID)
Data: fullData
REML criterion at convergence: 7374.9
Scaled residuals:
Min 1Q Median 3Q Max
-3.6191 -0.5154 0.1350 0.6102 2.6256
Random effects:
Groups Name Variance Std.Dev. Corr
subID (Intercept) 0.8484 0.9211
hub 0.6631 0.8143 -0.56
Residual 1.7554 1.3249
Number of obs: 2067, groups: subID, 211
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 5.14426 0.08313 180.94385 61.881 < 2e-16 ***
hub 0.63695 0.10737 199.75811 5.932 1.3e-08 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
hub -0.601
m<-lmer(Chan ~ hubW + ( hubW | subID), data=fullData)
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: Chan ~ hubW + (hubW | subID)
Data: fullData
REML criterion at convergence: 7370.9
Scaled residuals:
Min 1Q Median 3Q Max
-3.6127 -0.5366 0.1385 0.6163 2.6130
Random effects:
Groups Name Variance Std.Dev. Corr
subID (Intercept) 0.8089 0.8994
hubW 0.5306 0.7284 -0.53
Residual 1.7619 1.3274
Number of obs: 2067, groups: subID, 211
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 5.15300 0.07975 188.71394 64.614 < 2e-16 ***
hubW 0.69360 0.10529 232.85966 6.588 2.95e-10 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr)
hubW -0.548
fullShort <- do.call(data.frame, # Replace Inf in data by NA
lapply(fullShort,
function(x) replace(x, is.infinite(x), NA)))
corMat <- fullShort %>% select(edgeTot:NFC) %>% cor(fullShort,use="pairwise.complete.obs")
outphm <- pheatmap(corMat, fontsize_row = 6, fontsize_col = 6, angle_col = 45, angle_row =45, width=100, height = 200 )
heatmaply_cor(round(corMat,3), Rowv=outphm[[1]], Colv=outphm[[2]], revC=TRUE, fontsize_row = 2.5, fontsize_col = 2.5, angle_col = 45, angle_row =45, limits = c(-1, 1), colors = colorRampPalette(rev(brewer.pal(n = 7, name =
"RdYlBu")))(100) )
fullShort %>% select(vad_compAg, MAIA:NFC) %>% corToOne(., "vad_compAg")
[1] "All required packages attached"
Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
Note: Using an external vector in selections is ambiguous.
ℹ Use `all_of(referenceVar)` instead of `referenceVar` to silence this message.
ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
This message is displayed once per session.
fullShort %>% select(vad_compAg, MAIA:NFC) %>% plotCorToOne(., "vad_compAg")
[1] "All required packages attached"
Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(edgeTot, MAIA:NFC) %>% corToOne(., "edgeTot")
[1] "All required packages attached"
Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(edgeTot, MAIA:NFC) %>% plotCorToOne(., "edgeTot")
[1] "All required packages attached"
Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(numID, MAIA:NFC) %>% corToOne(., "numID")
[1] "All required packages attached"
Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(numID, MAIA:NFC) %>% plotCorToOne(., "numID")
[1] "All required packages attached"
Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(dense, MAIA:NFC) %>% corToOne(., "dense")
[1] "All required packages attached"
Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(dense, MAIA:NFC) %>% plotCorToOne(., "dense")
[1] "All required packages attached"
Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(aveDist, MAIA:NFC) %>% corToOne(., "aveDist")
[1] "All required packages attached"
Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(aveDist, MAIA:NFC) %>% plotCorToOne(., "aveDist")
[1] "All required packages attached"
Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(Val_1_Homoph, MAIA:NFC) %>% corToOne(., "Val_1_Homoph")
[1] "All required packages attached"
Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(Val_1_Homoph, MAIA:NFC) %>% plotCorToOne(., "Val_1_Homoph")
[1] "All required packages attached"
Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(Val_2_Homoph, MAIA:NFC) %>% corToOne(., "Val_2_Homoph")
[1] "All required packages attached"
Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(Val_2_Homoph, MAIA:NFC) %>% plotCorToOne(., "Val_2_Homoph")
[1] "All required packages attached"
Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(Fund_Homoph, MAIA:NFC) %>% corToOne(., "Fund_Homoph")
[1] "All required packages attached"
Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(Fund_Homoph, MAIA:NFC) %>% plotCorToOne(., "Fund_Homoph")
[1] "All required packages attached"
Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(Rep_Homoph, MAIA:NFC) %>% corToOne(., "Rep_Homoph")
fullShort %>% select(Rep_Homoph, MAIA:NFC) %>% plotCorToOne(., "Rep_Homoph")
summary(m)
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: PminN ~ CESD * outdegree + (1 | subID)
Data: fullData
REML criterion at convergence: 15416.7
Scaled residuals:
Min 1Q Median 3Q Max
-2.5919 -0.5456 0.1928 0.7810 1.9627
Random effects:
Groups Name Variance Std.Dev.
subID (Intercept) 377.3 19.43
Residual 3362.7 57.99
Number of obs: 1399, groups: subID, 202
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 47.371 11.856 331.376 3.996 7.95e-05 ***
CESD -8.022 5.160 329.247 -1.554 0.12103
outdegree 10.696 3.709 1362.306 2.884 0.00399 **
CESD:outdegree -4.841 1.611 1356.346 -3.006 0.00270 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) CESD outdgr
CESD -0.974
outdegree -0.524 0.512
CESD:outdgr 0.513 -0.527 -0.977
fullShort %>% select(recip, MAIA:NFC) %>% corToOne(., "recip")
fullShort %>% select(recip, MAIA:NFC) %>% plotCorToOne(., "recip")
fullShort %>% select(cohes, MAIA:NFC) %>% corToOne(., "cohes")
[1] "All required packages attached"
Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(cohes, MAIA:NFC) %>% plotCorToOne(., "cohes")
[1] "All required packages attached"
Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(modular, MAIA:NFC) %>% corToOne(., "modular")
[1] "All required packages attached"
Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(modular, MAIA:NFC) %>% plotCorToOne(., "modular")
[1] "All required packages attached"
Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(recip, MAIA:NFC) %>% corToOne(., "recip")
[1] "All required packages attached"
Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(recip, MAIA:NFC) %>% plotCorToOne(., "recip")
[1] "All required packages attached"
Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(sdDeg, MAIA:NFC) %>% corToOne(., "sdDeg")
[1] "All required packages attached"
Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(sdDeg, MAIA:NFC) %>% plotCorToOne(., "sdDeg")
[1] "All required packages attached"
Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(sdDegW, MAIA:NFC) %>% corToOne(., "sdDegW")
[1] "All required packages attached"
Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'
fullShort %>% select(sdDegW, MAIA:NFC) %>% plotCorToOne(., "sdDegW")
[1] "All required packages attached"
Correlation method: 'pearson'
Missing treated using: 'pairwise.complete.obs'